statsmodels.sandbox.distributions.extras.ACSkewT_gen.std ACSkewT_gen.std(*args, **kwds) Standard deviation of the distribution. Parameters: arg1, arg2, arg3,... : array_like The shape parameter(s) for the distribution (see docstring of the instance object for more information) loc : array_like, optional location parameter (default=0) scale : array_like, optional scale parameter (default=1) Returns: std : float standard deviation of the distribution

statsmodels.robust.robust_linear_model.RLMResults.save RLMResults.save(fname, remove_data=False) save a pickle of this instance Parameters: fname : string or filehandle fname can be a string to a file path or filename, or a filehandle. remove_data : bool If False (default), then the instance is pickled without changes. If True, then all arrays with length nobs are set to None before pickling. See the remove_data method. In some cases not all arrays will be set to None. Notes If remove_

statsmodels.discrete.discrete_model.DiscreteModel.from_formula classmethod DiscreteModel.from_formula(formula, data, subset=None, *args, **kwargs) Create a Model from a formula and dataframe. Parameters: formula : str or generic Formula object The formula specifying the model data : array-like The data for the model. See Notes. subset : array-like An array-like object of booleans, integers, or index values that indicate the subset of df to use in the model. Assumes df is a pandas.DataF

statsmodels.discrete.discrete_model.LogitResults.get_margeff LogitResults.get_margeff(at='overall', method='dydx', atexog=None, dummy=False, count=False) Get marginal effects of the fitted model. Parameters: at : str, optional Options are: ?overall?, The average of the marginal effects at each observation. ?mean?, The marginal effects at the mean of each regressor. ?median?, The marginal effects at the median of each regressor. ?zero?, The marginal effects at zero for each regressor. ?all?

statsmodels.tsa.ar_model.ARResults.cov_params ARResults.cov_params(r_matrix=None, column=None, scale=None, cov_p=None, other=None) Returns the variance/covariance matrix. The variance/covariance matrix can be of a linear contrast of the estimates of params or all params multiplied by scale which will usually be an estimate of sigma^2. Scale is assumed to be a scalar. Parameters: r_matrix : array-like Can be 1d, or 2d. Can be used alone or with other. column : array-like, optional Must be

statsmodels.genmod.generalized_estimating_equations.GEE.fit GEE.fit(maxiter=60, ctol=1e-06, start_params=None, params_niter=1, first_dep_update=0, cov_type='robust') [source] Fits a marginal regression model using generalized estimating equations (GEE). Parameters: maxiter : integer The maximum number of iterations ctol : float The convergence criterion for stopping the Gauss-Seidel iterations start_params : array-like A vector of starting values for the regression coefficients. If Non

statsmodels.genmod.families.family.Gaussian.predict Gaussian.predict(mu) Linear predictors based on given mu values. Parameters: mu : array The mean response variables Returns: lin_pred : array Linear predictors based on the mean response variables. The value of the link function at the given mu.

statsmodels.regression.mixed_linear_model.MixedLM.steepest_ascent MixedLM.steepest_ascent(params, n_iter) [source] Take steepest ascent steps to increase the log-likelihood function. Parameters: params : array-like The starting point of the optimization. n_iter: non-negative integer : Return once this number of iterations have occured. Returns: A MixedLMParameters object containing the final value of the : optimization. :

statsmodels.discrete.discrete_model.LogitResults.resid_dev static LogitResults.resid_dev() Deviance residuals Notes Deviance residuals are defined where and is the total number of observations sharing the covariate pattern . For now is always set to 1.

statsmodels.duration.hazard_regression.PHRegResults.llf static PHRegResults.llf()